Skip to main content

Metabolic Analysis of Metatranscriptomic Data from Planktonic Communities

Part of the Lecture Notes in Computer Science book series (LNBI,volume 10330)

Abstract

This paper describes an enhanced method for analyzing microbial metatranscriptomic (community RNA-seq) data using Expectation - Maximization (EM)-based differentiation and quantification of predicted gene, enzyme, and metabolic pathway activity. Here, we demonstrate the method by analyzing the metatranscriptome of planktonic communities in surface waters from the Northern Louisiana Shelf (Gulf of Mexico) during contrasting light and dark conditions. The analysis reveals that the level of transcripts encoding proteins of oxidative phosphorylation varys little between day and night. In contrast, transcripts of pyrimidine metabolism are significantly more abundant at night, whereas those of carbon fixation by photosynthetic organisms increase 2-fold in abundance from night to day.

Keywords

  • KEGG Orthology
  • Maximum Likelihood Model
  • Pathway Activity Level
  • Contig Abundance
  • Infer Pathway Activity

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-59575-7_41
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-59575-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Fig. 1.

References

  1. Donato, M., Xu, Z., Tomoiaga, A., Granneman, J.G., MacKenzie, R.G., Bao, R., Than, N.G., Westfall, P.H., Romero, R., Draghici, S.: Analysis and correction of crosstalk effects in pathway analysis. Genome Res. 23(11), 1885–1893 (2013)

    CrossRef  Google Scholar 

  2. Efron, B., Tibshirani, R.: On testing the significance of sets of genes. Ann. Appl. Stat. 1, 107–129 (2007)

    MathSciNet  CrossRef  MATH  Google Scholar 

  3. Huson, D.H., Mitra, S., Ruscheweyh, H.-J., Weber, N., Schuster, S.C.: Integrative analysis of environmental sequences using MEGAN4. Genome Res. 21(9), 1552–1560 (2011)

    CrossRef  Google Scholar 

  4. Konwar, K.M., Hanson, N.W., Pagé, A.P., Hallam, S.J.: MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information. BMC Bioinform. 14(1), 202 (2013)

    CrossRef  Google Scholar 

  5. Mitrea, C., Taghavi, Z., Bokanizad, B., Hanoudi, S., Tagett, R., Donato, M., Voichita, C., Dr, S.: Methods and approaches in the topology-based analysis of biological pathways. Front. Physiol. 4, 278 (2013)

    CrossRef  Google Scholar 

  6. Sharon, I., Bercovici, S., Pinter, R.Y., Shlomi, T.: Pathway-based functional analysis of metagenomes. J. Comput. Biol. 18(3), 495–505 (2011)

    MathSciNet  CrossRef  Google Scholar 

  7. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Nat. Acad. Sci. U.S.A. 102(43), 15545–15550 (2005)

    CrossRef  Google Scholar 

  8. Tarca, A.L., Draghici, S., Bhatti, G., Romero, R.: Down-weighting overlapping genes improves gene set analysis. BMC Bioinform. 13(1), 136 (2012)

    CrossRef  Google Scholar 

  9. Temate-Tiagueu, Y., Seesi, S.A., Mathew, M., Mandric, I., Rodriguez, A., Bean, K., Cheng, Q., Glebova, O., Măndoiu, I., Lopanik, N.B., Zelikovsky, A.: Inferring metabolic pathway activity levels from rna-seq data. BMC Genom. 17(5), 542 (2016). doi:10.1186/s12864-016-2823-y

    CrossRef  Google Scholar 

  10. Ye, Y., Doak, T.G.: A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput. Biol. 5(8), 1000465 (2009)

    CrossRef  Google Scholar 

  11. Huntemann, M., Ivanova, N.N., Mavromatis, K., Tripp, H.J., Paez-Espino, D., Tennessen, K., Palaniappan, K., Szeto, E., Pillay, M., Chen, I.-M.A., et al.: The standard operating procedure of the DOE-JGI metagenome annotation pipeline (MAP v. 4). Stan. Genomic Sci. 11(1), 17 (2016)

    CrossRef  Google Scholar 

  12. Mandric, I., Temate-Tiagueu, Y., Shcheglova, T., Seesi, S.A., Zelikovsky, A., Mandoiu, I.: Fast bootstrapping-based estimation of confidence intervals of expression levels and differential expression from RNA-SEQ data. Bioinformatics (to appear)

    Google Scholar 

  13. Al Seesi, S., Tiagueu, Y.T., Zelikovsky, A., Măndoiu, I.I.: Bootstrap-based differential gene expression analysis for RNA-SEQ data with and without replicates. BMC Genom. 15(8), 2 (2014)

    CrossRef  Google Scholar 

  14. Bray, N.L., Pimentel, H., Melsted, P., Pachter, L.: Near-optimal probabilistic RNA-SEQ quantification. Nat. Biotechnol. 34(5), 525–527 (2016)

    CrossRef  Google Scholar 

  15. Al Seesi, S., Mangul, S., Caciula, A., Zelikovsky, A., Măndoiu, I.: Transcriptome reconstruction and quantification from RNA sequencing data. Genome Anal.: Curr. Proced. Appl. 39 (2014)

    Google Scholar 

Download references

Acknowledgements

IM, SK and AZ were partially supported from NSF Grants 1564899 and 16119110, IM and SK were partially supported by GSU Molecular Basis of Disease Fellowship, IIM was partially supported from NSF Grants 1564936 and 1618347, CP and FS were partially supported by NSF Grants 1151698, 1558916, and 1564559, and Simons Foundation award 346253.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alex Zelikovsky .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Mandric, I., Knyazev, S., Padilla, C., Stewart, F., Măndoiu, I.I., Zelikovsky, A. (2017). Metabolic Analysis of Metatranscriptomic Data from Planktonic Communities. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59575-7_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59574-0

  • Online ISBN: 978-3-319-59575-7

  • eBook Packages: Computer ScienceComputer Science (R0)